Conditional Logistic Regression Models for Correlated Binary Data
Copyright policy )Conditional Logistic Regression Models for Correlated Binary Data
A class of conditional logistic regression models for clustered binary data is considered. This includes the polychotomous logistic model of \textit{B. Rosner} [Biometrics 40, 1025-1035 (1984)] as a special case. Properties such as the joint distribution and pairwise odds ratio are investigated. A class of easily computed estimating functions is introduced which is shown to have high efficiency compared to the computationally intensive maximum likelihood approach. An example on chronic obstructive pulmonary disease among sibs is presented for illustration.
- Johns Hopkins University United States
clustered binary data, conditional logistic regression models, joint distribution, estimating functions, Applications of statistics to biology and medical sciences; meta analysis, chronic obstructive pulmonary disease, Linear inference, regression, efficiency, General nonlinear regression, polychotomous logistic model, pairwise odds ratio
clustered binary data, conditional logistic regression models, joint distribution, estimating functions, Applications of statistics to biology and medical sciences; meta analysis, chronic obstructive pulmonary disease, Linear inference, regression, efficiency, General nonlinear regression, polychotomous logistic model, pairwise odds ratio
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